Michael GrayPACS/VNAPACS Paradigm Shift: Part 3 -- The advent of PACS 3.0In the final article in his series on enterprise medical image viewing, healthcare IT expert Michael Gray describes the PACS 3.0 concept and explores the benefits of making the switch to this new PACS model.October 8, 2014PACS/VNAPACS Paradigm Shift: Part 2 -- VNAs, viewers, and radiology PACSIn part 2 of his series on enterprise medical image viewing, healthcare IT expert Michael Gray discusses the effect of vendor-neutral archives (VNAs) and universal viewers on the current image management paradigm, which has resulted in what he describes as the "step-by-step dismemberment" of traditional radiology-oriented PACS.September 28, 2014PACS/VNAPACS Paradigm Shift: Part 1 -- Problems with today's PACSHealthcare delivery organizations are experiencing a paradigm shift from departmental views of medical images -- typically seen in radiology and cardiology -- to a single, enterprise view that encompasses all medical images. In this article, healthcare IT expert Michael Gray explores some of the challenges that these changes present.September 23, 2014PACS/VNABuilding the Best RFP: Part III -- Data migration prenuptialAuntMinnie.com presents the third installment in the Building the Best RFP series. The articles focus on major technology issues involved in choosing the right PACS, and each edition includes the precise wording you might use in an RFP to a vendor. In part three, PACS consultant Michael Gray discusses data migration.October 10, 2007PACS/VNABuilding the Best RFP: Part 2 -- Display softwareAuntMinnie.com is pleased to present the second installment in the Building the Best RFP series. This new series of articles focuses on the major technology issues in choosing the right PACS, and each edition will include the precise wording you might use in an RFP to a vendor. In part two, PACS consultant Michael Gray discusses different display software packages and installation.July 15, 2007PACS/VNABuilding the Best RFP: Part 1 -- Directory architectureAuntMinnie.com is pleased to present the first installment in the Building the Best RFP series. This new series of articles will focus on the major technology issues in choosing the right PACS, and each installment will include the precise wording you might use in an RFP to a vendor. In part one, PACS consultant Michael Gray discusses directory server architecture.May 14, 2007Page 1 of 1Top StoriesWomens ImagingWomen's Imaging MinnieCast, Episode 8: New NCCN guidance on AI and breast cancer riskBethany Niell, MD, PhD, talks new guidance on breast cancer screening by the NCCN, which includes image-based AI risk assessment.Digital X-RayLLMs excel at scoliosis detection on spine x-raysHomeExpanding opportunities for interventional radiology practiceCTWelding fumes linked to distinct lung CT changes, functionSponsor ContentTurning Innovation into Everyday Efficiency
![Overview of the study design. (A) The fully automated deep learning framework was developed to estimate body composition (BC) (defined as subcutaneous adipose tissue [SAT] in liters; visceral adipose tissue [VAT] in liters; skeletal muscle [SM] in liters; SM fat fraction [SMFF] as a percentage; and intramuscular adipose tissue [IMAT] in deciliters) from MRI. The fully automated framework comprised one model (model 1) to quantify different BC measures (SAT, VAT, SM, SMFF, and IMAT) as three-dimensional (3D) measures from whole-body MRI scans. The second model (model 2) was trained to identify standardized anatomic landmarks along the craniocaudal body axis (z coordinate field), which allowed for subdividing the whole-body measures into different subregions typically examined on clinical routine MRI scans (chest, abdomen, and pelvis). (B) BC was quantified from whole-body MRI in over 66,000 individuals from two large population-based cohort studies, the UK Biobank (UKB) (36,317 individuals) and the German National Cohort (NAKO) (30,291 individuals). Bar graphs show age distribution by sex and cohort. BMI = body mass index. (C) After the performance assessment of the fully automated framework, the change in BC measures, distributions, and profiles across age decades were investigated. Age-, sex-, and height-adjusted body composition reference curves were calculated and made publicly available in a web-based z-score calculator (https://circ-ml.github.io).](https://img.auntminnie.com/mindful/smg/workspaces/default/uploads/2026/05/body-comp.XgAjTfPj1W.jpg?auto=format%2Ccompress&dpr=2&fit=crop&h=167&q=70&w=250)